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Record W2007768815 · doi:10.1177/0959354306070535

Literary Realism and Psychology

2006· article· en· W2007768815 on OpenAlexaff
Joseph L. Mills

Bibliographic record

VenueTheory & Psychology · 2006
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRealismEpistemologyPsychologySociologyLiterary scienceOrder (exchange)Philosophical realismLiterary criticismSocial psychologyPsychoanalysisLiteraturePhilosophyArt

Abstract

fetched live from OpenAlex

Moghaddam has suggested that psychologists could profit from the study of fiction. Although agreeing with Moghaddam in general, I feel it necessary to specify, fairly closely, the type of fiction which might be susceptible to psychological analysis. I choose literary realism, as defined by Wellek, as a possible area for fruitful analysis. Having discounted the difficulties possibly besetting such an analysis, I give some examples of the ways in which the study of the works of those working in the literary realist tradition could enlarge and enhance our understanding of psychological processes. Ultimately, however, I conclude that, in order to benefit fully from such a study, psychology would have to undergo a conceptual transformation since the two areas into which, ostensibly, literary knowledge could be incorporated (personality theory and the practice and theory of psychotherapy) are not constituted in such a way that such incorporation is possible.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0050.043
Scholarly communication0.0080.007
Open science0.0010.005
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0060.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.388
Teacher spread0.370 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2006
Admission routes1
Has abstractyes

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